{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LASSO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\Anaconda3\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "C:\\Users\\Lenovo\\Anaconda3\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n",
      "C:\\Users\\Lenovo\\Anaconda3\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(400, 11)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Cards</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Student</th>\n",
       "      <th>Married</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.891</td>\n",
       "      <td>3606</td>\n",
       "      <td>283</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106.025</td>\n",
       "      <td>6645</td>\n",
       "      <td>483</td>\n",
       "      <td>3</td>\n",
       "      <td>82</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Asian</td>\n",
       "      <td>903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104.593</td>\n",
       "      <td>7075</td>\n",
       "      <td>514</td>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "      <td>11</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Asian</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>148.924</td>\n",
       "      <td>9504</td>\n",
       "      <td>681</td>\n",
       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Asian</td>\n",
       "      <td>964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>55.882</td>\n",
       "      <td>4897</td>\n",
       "      <td>357</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "      <td>16</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Income  Limit  Rating  Cards  Age  Education  Gender Student Married  \\\n",
       "1   14.891   3606     283      2   34         11    Male      No     Yes   \n",
       "2  106.025   6645     483      3   82         15  Female     Yes     Yes   \n",
       "3  104.593   7075     514      4   71         11    Male      No      No   \n",
       "4  148.924   9504     681      3   36         11  Female      No      No   \n",
       "5   55.882   4897     357      2   68         16    Male      No     Yes   \n",
       "\n",
       "   Ethnicity  Balance  \n",
       "1  Caucasian      333  \n",
       "2      Asian      903  \n",
       "3      Asian      580  \n",
       "4      Asian      964  \n",
       "5  Caucasian      331  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r'E:\\programming\\dataset\\Into_to_statstical_learning\\Credit.csv',index_col=0)\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data[['Income','Limit','Rating','Student']]\n",
    "y = data['Balance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Student</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.891</td>\n",
       "      <td>3606</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
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       "      <td>106.025</td>\n",
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       "      <td>483</td>\n",
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       "      <td>104.593</td>\n",
       "      <td>7075</td>\n",
       "      <td>514</td>\n",
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       "      <td>148.924</td>\n",
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       "      <td>55.882</td>\n",
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      ],
      "text/plain": [
       "    Income  Limit  Rating  Student\n",
       "1   14.891   3606     283        0\n",
       "2  106.025   6645     483        1\n",
       "3  104.593   7075     514        0\n",
       "4  148.924   9504     681        0\n",
       "5   55.882   4897     357        0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['Student'] = X['Student'].map({'Yes':1,'No':0})\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept is  520.0150000000001\n",
      "Coef are  [-279.65262476  280.51607343  338.48984662  126.80051361]\n"
     ]
    }
   ],
   "source": [
    "# Linear regression (least square fit)\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_scaled,y)\n",
    "print('Intercept is ',lr.intercept_)\n",
    "print('Coef are ',lr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept is  520.0150000000001\n",
      "Coef are  [-274.81085729  282.07100696  332.10587107  125.70186799]\n"
     ]
    }
   ],
   "source": [
    "# Lasso\n",
    "lasso = Lasso(alpha = 1)\n",
    "lasso.fit(X_scaled,y)\n",
    "print('Intercept is ',lasso.intercept_)\n",
    "print('Coef are ',lasso.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can see for alpha = 1, we are having almost same value, there is very less effect of regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept is  520.0150000000001\n",
      "Coef are  [-230.81163473  248.47592676  321.75567292  115.61500508]\n"
     ]
    }
   ],
   "source": [
    "#increasing the values of alpha\n",
    "lasso = Lasso(alpha = 10)\n",
    "lasso.fit(X_scaled,y)\n",
    "print('Intercept is ',lasso.intercept_)\n",
    "print('Coef are ',lasso.coef_)\n",
    "# see the values decreasing, and moving towards zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept is  520.015\n",
      "Coef are  [0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "# Large value of alpha\n",
    "lasso = Lasso(alpha = 1000)\n",
    "lasso.fit(X_scaled,y)\n",
    "print('Intercept is ',lasso.intercept_)\n",
    "print('Coef are ',lasso.coef_)\n",
    "# all the values are 0 now. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://seaborn.pydata.org/generated/seaborn.lineplot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = [1e-1,1e1,1e2,1e5,1e7,1e9]\n",
    "intercept_dict = {}\n",
    "\n",
    "for a in alpha:\n",
    "    lasso = Lasso(alpha = a)\n",
    "    lasso.fit(X_scaled,y)\n",
    "    intercept_dict[a] = list(lasso.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "                       0           1           2           3\n",
       "1.000000e-01 -279.210780  285.430515  333.140891  126.710554\n",
       "1.000000e+01 -230.811635  248.475927  321.755673  115.615005\n",
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     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = pd.DataFrame(intercept_dict).T\n",
    "tmp.index = alpha\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Alpha')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16,8))\n",
    "sns.lineplot(data = tmp)\n",
    "plt.axhline(y = 0,linestyle = 'dashed',lw = 0.1,color = 'black')\n",
    "plt.xticks(alpha)\n",
    "plt.xlim(0,10)\n",
    "plt.ylabel('Coeffients')\n",
    "plt.xlabel('Alpha')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Alpha')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using log scale for x axis\n",
    "\n",
    "plt.figure(figsize = (16,8))\n",
    "sns.lineplot(data = tmp)\n",
    "plt.axhline(y = 0,linestyle = 'dashed',lw = 0.1,color = 'black')\n",
    "plt.xticks(alpha)\n",
    "plt.xscale('log')\n",
    "plt.ylabel('Coeffients')\n",
    "plt.xlabel('Alpha')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the graphs are not smooth as i have choosen less"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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